Empathy injection for question-answering systems

ABSTRACT

Exemplary methods and devices herein receive an inquiry and automatically analyze words used in the inquiry, potential answers, and data maintained by evidence sources using the computerized device to determine the sensitivity level associated with the inquiry. The sensitivity level associated with the inquiry represents an emotional and cognitive state of the user. Such methods and devices automatically generate at least one follow-up question based on the sensitivity level associated with the inquiry and receive a follow-up response into the computerized device in response to the follow-up question(s). The methods and devices also automatically produce scores for the potential answers using the computerized device based on the inquiry, the follow-up responses, and ratings of the evidence sources. Following this, these methods and devices automatically generate output answers to the inquiry based on the sensitivity level associated with the inquiry using the computerized device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. Ser. No. 14/058,704 filed onOct. 21, 2013, which is a Continuation of U.S. Pat. No. 9,298,766, thecomplete disclosure of which, in its entirety, is herein incorporated byreference.

BACKGROUND

The present disclosure relates to question answering systems andprovides methods for returning an answer to a query in an empatheticmanner based on a determination that the answer may evoke a negativeemotional state.

Computerized devices are used for many purposes to improve the qualityof people's lives. In one example, computerized systems canautomatically answer questions presented by users in natural languageformat. Such systems are highly useful because they provide a morenatural computer interface, thereby improving the user experience.

SUMMARY

Exemplary methods herein receive an inquiry from a user into acomputerized device. Such methods automatically perform an initialanalysis of the inquiry using the computerized device to producepotential answers to the inquiry from evidence sources. Also, suchmethods can automatically obtain biometric information from biometricdevices operatively connected to the computerized device.

These methods automatically analyze words used in the inquiry, thepotential answers, data maintained by the evidence sources, thebiometric information, etc., using the computerized device to determinethe sensitivity level associated with the inquiry. For example, thesemethods can automatically identify words associated with a specificemotional or cognitive state. Such methods can also automaticallyidentify sentence structure associated with a specific emotional orcognitive state, or automatically identify relationships between datamaintained by the evidence sources and emotionally significantphenomena. Such methods can also automatically detect negative sentimentassociated with one of the potential answers. The sensitivity levelassociated with the inquiry thus represents an emotional and cognitivestate of the user as automatically determined by the computerizeddevice.

Such methods can automatically generate at least one follow-up questionbased on the sensitivity level associated with the inquiry using thecomputerized device to refine the potential answers. When generating thefollow-up question, these methods can generate relatively moreempathetic follow-up questions or relatively less empathetic follow-upquestions based on the sensitivity level associated with the inquiry.The relatively more empathetic follow-up questions and the relativelyless empathetic follow-up questions are distinguished from each other bythe amount of information presented and by the word choice used whenpresenting the information.

Then, these methods automatically output the follow-up question(s) usingthe computerized device in response to the inquiry and, in turn, receiveat least one follow-up response into the computerized device in responseto the follow-up question(s). In one example, these methods can outputthe follow-up questions using an avatar. The avatar changes appearanceand mannerisms based on the sensitivity level associated with theinquiry.

Such methods also automatically produce scores for the potential answersusing the computerized device based on the inquiry, the follow-upresponses, the ratings of the evidence sources, etc. Following this,these methods automatically generate output answers to the inquiry basedon the sensitivity level associated with the inquiry using thecomputerized device to refine the potential answers. When generating theoutput answers, such methods generate relatively more empathetic outputanswers or relatively less empathetic output answers based on thesensitivity level associated with the inquiry. Again, the relativelymore empathetic output answers and the relatively less empathetic outputanswers are distinguished from each other by the amount of informationpresented and the word choice used when presenting information.

Then, such methods automatically output the output answers, the scores,and links to the evidence sources from the computerized device. Again,these methods can output the answers, the scores, the links, etc., usingan avatar. The avatar changes appearance and mannerisms based on thesensitivity level associated with the inquiry. In addition, such methodscan output the emotional and cognitive state of the user asautomatically determined by the computerized device to individuals otherthan the user, and can make recommendations to such individuals based onthe emotional and cognitive state of the user.

Other methods herein receive an inquiry from a user into a computerizeddevice. Such methods automatically perform an initial analysis of theinquiry using the computerized device to produce potential answers tothe inquiry from evidence sources. Also, such methods can automaticallyobtain biometric information from biometric devices operativelyconnected to the computerized device.

These methods automatically analyze words used in the inquiry, thepotential answers, data maintained by the evidence sources, thebiometric information, etc., using the computerized device to determinethe sensitivity level associated with the inquiry. For example, thesemethods can automatically identify words associated with a specificemotional or cognitive state. Such methods can also automaticallyidentify sentence structure associated with a specific emotional orcognitive state, or automatically identify relationships between datamaintained by the evidence sources and emotionally significantphenomena. Such methods can also automatically detect negative sentimentassociated with one of the potential answers. The sensitivity levelassociated with the inquiry thus represents an emotional and cognitivestate of the user as automatically determined by the computerizeddevice.

Such methods can automatically generate at least one follow-up questionbased on the sensitivity level associated with the inquiry using thecomputerized device to refine the potential answers. When generating thefollow-up question, these methods can generate relatively moreempathetic follow-up questions or relatively less empathetic follow-upquestions based on the sensitivity level associated with the inquiry.The relatively more empathetic follow-up questions and the relativelyless empathetic follow-up questions are distinguished from each other bythe amount of information presented and by the word choice used whenpresenting the information.

Then, these methods automatically output the follow-up question(s) usingthe computerized device in response to the inquiry and, in turn, receiveat least one follow-up response into the computerized device in responseto the follow-up question(s). In one example, these methods can outputthe follow-up questions using an avatar. The avatar changes appearanceand mannerisms based on the sensitivity level associated with theinquiry.

Such methods automatically analyze words used in the follow-up responseusing the computerized device to refine the sensitivity level associatedwith the inquiry. Such methods also automatically produce scores for thepotential answers using the computerized device based on the inquiry,the follow-up responses, the ratings of the evidence sources, etc.

Following this, these methods automatically generate output answers tothe inquiry based on the sensitivity level associated with the inquiryusing the computerized device to refine the potential answers. Whengenerating the output answers, such methods generate relatively moreempathetic output answers or relatively less empathetic output answersbased on the sensitivity level associated with the inquiry. Again, therelatively more empathetic output answers and the relatively lessempathetic output answers are distinguished from each other by theamount of information presented and the word choice used when presentinginformation.

Then, such methods automatically output the output answers, the scores,and links to the evidence sources from the computerized device. Again,these methods can output the answers, the scores, the links, etc., usingan avatar. The avatar changes appearance and mannerisms based on thesensitivity level associated with the inquiry. In addition, such methodscan output the emotional and cognitive state of the user asautomatically determined by the computerized device to individuals otherthan the user, and can make recommendations to such individuals based onthe emotional and cognitive state of the user.

Question-answer systems herein comprise a graphic user interfacereceiving an inquiry from a user, biometric devices receiving biometricinformation from the user, and a processor operatively connected to thegraphic user interface and biometric devices. The processorautomatically performs an initial analysis of the inquiry to producepotential answers to the inquiry from evidence sources.

The processor automatically analyzes words used in the inquiry, thepotential answers, data maintained by the evidence sources, thebiometric information, etc., to determine the sensitivity levelassociated with the inquiry. For example, the processor canautomatically identify words associated with a specific emotional orcognitive state. Such a processor can also automatically identifysentence structure associated with a specific emotional or cognitivestate, or automatically identify relationships between data maintainedby the evidence sources and emotionally significant phenomena. Theprocessor can also automatically detect negative sentiment associatedwith one of the potential answers. The sensitivity level associated withthe inquiry thus represents an emotional and cognitive state of the useras automatically determined by the computerized device.

The processor can further generate at least one follow-up question basedon the sensitivity level associated with the inquiry to refine thepotential answers. When generating the follow-up question(s), theprocessor can generate relatively more empathetic follow-up questions orrelatively less empathetic follow-up questions based on the sensitivitylevel associated with the inquiry. The relatively more empatheticfollow-up questions and the relatively less empathetic follow-upquestions are distinguished from each other by the amount of informationpresented and by the word choice used when presenting the information.

Then, the graphic user interface automatically outputs the follow-upquestion(s) in response to the inquiry and, in turn, receives at leastone follow-up response in response to the follow-up question(s). In oneexample, the graphic user interface can output the follow-up questionsusing an avatar. The avatar changes appearance and mannerisms based onthe sensitivity level associated with the inquiry.

The processor automatically analyzes words used in the follow-upresponse to refine the sensitivity level associated with the inquiry.The processor also automatically produces scores for the potentialanswers based on the inquiry, the follow-up responses, the ratings ofthe evidence sources, etc.

Following this, the processor automatically generates output answers tothe inquiry based on the sensitivity level associated with the inquiryto refine the potential answers. When generating the output answers, theprocessor generates relatively more empathetic output answers orrelatively less empathetic output answers based on the sensitivity levelassociated with the inquiry. Again, the relatively more empatheticoutput answers and the relatively less empathetic output answers aredistinguished from each other by the amount of information presented andthe word choice used when presenting information.

Then, the graphic user interface automatically outputs the outputanswers, the scores, and links to the evidence sources from thecomputerized device. Again, the graphic user interface can output theanswers, the scores, the links, etc., using an avatar. The avatarchanges appearance and mannerisms based on the sensitivity levelassociated with the inquiry. In addition, the graphic user interface canoutput the emotional and cognitive state of the user as automaticallydetermined by the computerized device to individuals other than theuser, and can make recommendations to such individuals based on theemotional and cognitive state of the user.

In other embodiments, a tangible (non-transitory) computer-readablestorage media can tangibly store instructions executable by acomputerized device reading such instructions from the tangiblecomputer-readable storage media. Such instructions cause thecomputerized device to perform methods that receive an inquiry from auser into a computerized device. Such methods automatically perform aninitial analysis of the inquiry using the computerized device to producepotential answers to the inquiry from evidence sources. Also, suchmethods can automatically obtain biometric information from biometricdevices operatively connected to the computerized device.

These methods automatically analyze words used in the inquiry, thepotential answers, data maintained by the evidence sources, thebiometric information, etc., using the computerized device to determinethe sensitivity level associated with the inquiry. For example, thesemethods can automatically identify words associated with a specificemotional or cognitive state. Such methods can also automaticallyidentify sentence structure associated with a specific emotional orcognitive state, or automatically identify relationships between datamaintained by the evidence sources and emotionally significantphenomena. Such methods can also automatically detect negative sentimentassociated with one of the potential answers. The sensitivity levelassociated with the inquiry thus represents an emotional and cognitivestate of the user as automatically determined by the computerizeddevice.

Such methods can automatically generate at least one follow-up questionbased on the sensitivity level associated with the inquiry using thecomputerized device to refine the potential answers. When generating thefollow-up question, these methods can generate relatively moreempathetic follow-up questions or relatively less empathetic follow-upquestions based on the sensitivity level associated with the inquiry.The relatively more empathetic follow-up questions and the relativelyless empathetic follow-up questions are distinguished from each other bythe amount of information presented and by the word choice used whenpresenting the information.

Then, these methods automatically output the follow-up question(s) usingthe computerized device in response to the inquiry and, in turn, receiveat least one follow-up response into the computerized device in responseto the follow-up question(s). In one example, these methods can outputthe follow-up questions using an avatar. The avatar changes appearanceand mannerisms based on the sensitivity level associated with theinquiry.

Such methods automatically analyze words used in the follow-up responseusing the computerized device to refine the sensitivity level associatedwith the inquiry. Such methods also automatically produce scores for thepotential answers using the computerized device based on the inquiry,the follow-up responses, the ratings of the evidence sources, etc.

Following this, these methods automatically generate output answers tothe inquiry based on the sensitivity level associated with the inquiryusing the computerized device to refine the potential answers. Whengenerating the output answers, such methods generate relatively moreempathetic output answers or relatively less empathetic output answersbased on the sensitivity level associated with the inquiry. Again, therelatively more empathetic output answers and the relatively lessempathetic output answers are distinguished from each other by theamount of information presented and the word choice used when presentinginformation.

Then, such methods automatically output the output answers, the scores,and links to the evidence sources from the computerized device. Again,these methods can output the answers, the scores, the links, etc., usingan avatar. The avatar changes appearance and mannerisms based on thesensitivity level associated with the inquiry. In addition, such methodscan output the emotional and cognitive state of the user asautomatically determined by the computerized device to individuals otherthan the user, and can make recommendations to such individuals based onthe emotional and cognitive state of the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments herein will be better understood from the followingdetailed description with reference to the drawings, which are notnecessarily drawn to scale and in which:

FIG. 1 is a flow diagram illustrating embodiments herein;

FIG. 2 is a flow diagram illustrating embodiments herein;

FIG. 3 is a schematic diagram of a hardware system according toembodiments herein;

FIG. 4 is a schematic diagram of a deployment system according toembodiments herein;

FIG. 5 is a schematic diagram of an integration system according toembodiments herein;

FIG. 6 is a schematic diagram of an on demand system according toembodiments herein;

FIG. 7 is a schematic diagram of a virtual private network systemaccording to embodiments herein; and

FIG. 8 is a schematic diagram of a virtual private network systemaccording to embodiments herein.

DETAILED DESCRIPTION

The systems and methods herein relate to question answering systems.Specifically, the systems and methods herein provide methods forreturning an answer to a query in an empathetic manner based on adetermination that the answer may evoke a negative emotional state. Forexample, if a question answering system is developed to provide medicalanswers to a patient based on the patient's medical information, thesystem may generate the most correct answer, but not a result thepatient wants. In other words, the result may be “unfavorable news.” Ina medical context where a doctor is providing “unfavorable news” to apatient the doctor is able to provide the news in an empathetic manner.Therefore, the question answering systems and methods described belowprovide the ability to detect whether an answer is “unfavorable news”based on many different factors, and deliver the result in an empatheticmanner when needed. The systems and methods herein determine whether theanswer may invoke a negative user response and, if this occurs, providethe result in an empathetic manner.

In one example, the methods and systems herein are useful withquestion-and-answering systems in a healthcare setting by automaticallyassessing a user's emotional and cognitive state when presenting medicalinformation to the user. However, the methods and systems herein are notlimited to the healthcare field, and are instead applicable to allquestion/answer systems that respond to user's inquiries.

Based on the assessment of the user's emotional and cognitive state, themethods and systems herein can 1) tailor information with respect to theuse of empathetic language and 2) optionally provide suggestions tospecific healthcare professionals based on empathy assessments.Extensions to avatar use in virtual universes are also included insystems herein. The way in which information is presented (e.g., withrespect to empathy) can affect the degree to which a patient assimilatessensitive or serious health information and also the way in which thepatient communicates health information.

The systems and methods herein work with question/answer systems, i.e.,those systems which answer natural language questions by querying datarepositories and applying elements of language processing, informationretrieval, and machine learning to arrive at a conclusion. Specifically,the systems and methods herein present information or answers that havea higher value than that developed by a medical information repositoryquery alone. The features herein use components that are automaticallytriggered based on criteria.

Regarding the importance of empathy in information presentation, intraditional physician-patient interactions (e.g., “bedside manner”) thebetter the relationship in terms of emotional connection, respect, andshared values, the more useful will be the sharing of information (e.g.,about a patient's disease) that is assimilated by the patient and alsocommunicated to the healthcare professional. This in turn providesfeedback in which the accuracy of diagnosis is improved as well as thepatient's sense of well-being. When this relationship is poor, patientand physician distrust can occur, and patients may be less likely toappreciate the information or properly act upon receiving suchinformation. Also, if the patient is in a poor emotional state (such asa state of agitation, apprehension, mistrust, or an acute psychologicaldisturbance due to side effects of their condition, e.g., a drugreaction, post-operative, or post-partum depression, etc.) this cansometimes exacerbate disease symptoms, cause symptoms to deteriorate,and affect information exchange between patient and healthcare agents.

Although computerized question/answer systems are being developed to aidhealthcare professionals in making diagnoses, in the future end-usersmay use similar systems for guidance when selecting healthcareprofessionals and to gain a high-level understanding of symptoms andoptions. Today, people frequently make use of information discovered onthe Internet when first start seeking an understanding of possiblediseases related to themselves, relatives, colleagues and friends.Depending on the personality and background of the individual seeking togain knowledge of disease and injury processes, such information may befrightening, or may be used inappropriately, etc. For example, someindividuals who are nervous and/or depressed may become overly fearful.

With respect to empathy injection, methods and systems disclosed hereinprovide an enhancement of question/answer systems that will modify theirresponses to user's queries depending on information associated with thepsychological states of users of the system. For example, some users maybe able to understand and emotionally handle information related topossible rare diseases, cancer, etc., better than others who should bepresented with information in a more empathetic fashion. Empatheticlanguage might include phrases such as, “We know that this diseaseinformation can be frightening, but this disease is rare. Just pleasesee your physician.” In addition, the question/answer systems mayconsider the assessed psychological states as inputs to the system usedto suggest diagnoses and treatments to a healthcare provider. Forexample, when used by a physician, such systems can present informationin three columns, with the following preamble: “This patient appears tobe suffering acute depression, based on her answers to certainquestions. Therefore, the prescribed treatments may be best evaluatedbased on your further determination of to what extent she isdepressed: 1) Severe depressions, 2) Moderate depression or 3) Milddepression. Additionally, you may wish to present the information to herin the following manner.”

Thus, one aspect of this disclosure is a method of computing f(E)(emotional impact of a response). For example, “brain” may be a happyanswer if the question is asking what part of the body will benefit froma given nutritional supplement, but a sad answer if asking what part ofthe body will be damaged by a patient's illness. The systems and methodsherein can address this by detecting the emotional content at run timeusing supporting evidence to conclude that “brain” was the answer to thequestion being asked (in addition to context-independent information).

Finally, an aspect of drug development and regulatory approval ofgrowing importance is the use of companion diagnostics. A companiondiagnostic is a test that is performed on a patient prior toadministering certain drugs. The test may, for example, be genetic, andmay categorize the patient as being in a specific population ofindividuals for whom the drug has been approved for certain uses.Without such a test, the drug may not be administered. While companiondiagnostics typically do not include an assessment of a drug recipient'scognitive state, it is possible with better availability of formal meansto measure such states (such as those described in this disclosure)companion diagnostics may be devised with these states as inputs.Therefore, the systems and methods herein are applicable not only totailoring the presentation of information from a question/answer systembased on the patient's cognitive state, but also generate inputs to acompanion diagnostic system as it outputs specific insights fordiagnosis and treatment. Thus, the systems and methods herein providecompliance with certain companion diagnostics, based on the observedcognitive states of the users.

An overview of the methods and systems herein is shown in flowchart formin FIG. 1, using a patient and healthcare provider example. In item 300,one or more queries are received (e.g., concerning medical symptoms andconditions) from a user. The systems and methods herein (e.g., thedigital physician or DP) analyzes the inputs (textual or otherwise) anddetermine a useful answer (item 302) e.g., diagnosis, via establishedquestion/answer techniques. In item 304, the systems and methods hereinanalyze and monitor the emotional state (E) of the user (e.g., with userprofile/query characteristics, biometrics, analysis of language,questionnaires, reports from nursing staff and family members, foodintake information, etc.) to generate f(E) (the emotional impact of theanswer).

In item 304, the emotional state or personality type may be estimated bythe system accessing a user's profile, by an assessment of naturallanguage used by the person, by biometrics, by a history of previousinteractions, by an analysis of word combinations used for similar usersetc. A user profile, stored on a device, may specify various patientattributes including personality type, comprehensive medical records, orsome subset of attributes such as previous diagnoses and disease state(e.g., advanced vs. mild), user nationality, language, or occupation,and other such patient-specific information. This profile may be queriedto aid the system in providing a relevant and/or higher-confidenceanswer in an appropriate manner. Information associated with personswith known circumstances similar to that of question initiator (e.g.,via medical diagnoses, occupation, or expertise) may be also used tocreate an appropriate empathetic tone of response.

In certain circumstances, the system may not have high confidence that apatient's emotional or cognitive state has been adequately characterizedor assessed, and under these circumstances the systems and methodsherein apply an active learning component. This active learningcomponent can access specific information about individuals who havebeen in contact with the patient recently, including, but not limitedto, nursing staff members, doctors, family members and other visitors,and can direct to these individuals specific questions aimed at aidingthe machine learning system to perform this categorization. In somesense, this represents a collaborative input to the question/answersystem. If desired, none of this additional information will be obtainedwithout patient consent or without respect to all relevant privacy rulesand regulations. For example, with the patient's informed consent, thesystem may send an email to a spouse with the inquiry, “Based on an AIassessment of your wife's mental state, it is possible she is acutelydepressed. To aid in furthering this assessment, could you indicate ifyou have observed any of the following: 1) increased agitation, 2)increased sensitivity to stress, or 3) unresponsiveness to normalcommunication.” Based on these inquiries, the machine learning systemcan incorporate the active learning component's determinations into itsfuture assessments of the patient or other patients' cognitive oremotional states. Therefore, the methods and systems herein can use theactive learning component to access specific information provided byother individuals, modify the existing system for estimating a patient'semotional state, and make recommendations about the patient to thoseindividuals based on the emotional and cognitive state of said patientand the output from the active learning component and the modifiedsystem.

In some specific examples, f(E) can be computed in many ways (assuming aparticular value for a predetermined threshold value (Thresh)). In oneexample, a domain expert can create an informal account ofcharacteristics that are predictive of a high emotional state, and anaccount of what combinations of these characteristics are necessary toconclude a high emotional state. A programmer can then implement f(E) sothat it exceeds Thresh as required by the specification. In anotherexample, a domain expert can create a formal account of characteristicsthat are predictive of a high emotional state, and an account of whatcombinations of these characteristics are necessary to conclude a highemotional state using a formal rule language. The rule engine is thusable to directly compute f(E).

In additional examples, a domain expert can create an informal accountof characteristics that are predictive of a high emotional state, but donot provide an account of what combinations of these characteristics arenecessary to conclude a high emotional state. A programmer can implementdetectors for each of the characteristics. The system is given traininginstances of input data (“biometrics, analysis of language,questionnaires, reports from nursing staff and family members, foodintake information, etc.”, as noted above) along with manual judgmentsby experts as to whether each patient in the training set is in a highemotional state. Statistical machine learning techniques are used toconstruct an f(E) that best combines the results of the detectors to fitthe manual judgments in the training data to whether f(E)>Thresh.

The foregoing examples can be combined. Thus, a domain expert can createa formal account of characteristics that are predictive of a highemotional state (but do not provide an account of what combinations ofthese characteristics are necessary to conclude a high emotional state).The rule engine is thus able to compute values that are relevant tof(E). The system is given training instances of input data along withmanual judgments by experts as to whether each patient in the trainingset is in a high emotional state. Statistical machine learningtechniques are used to construct an f(E) that best combines the resultsof the rule engine to fit the manual judgments in the training data towhether f(E)>Thresh.

One component of systems and methods herein (operating in item 304)includes a manually generated list of answers that are known to beassociated with various emotional states in response to various types ofquestions. This component labels the answer with one or more numericalfeatures indicating the emotional content of the answer. For example,for questions asking for the diagnosis of a patient, answers such as“healthy”, “influenza”, “meningitis”, and “paranoid schizophrenia” allimply different emotional consequences for that patient.

Another component of systems and methods herein (operating in item 304)includes an automatically mined list of terms that occurdisproportionately in a corpus of text indicating negative sentiment ina training corpus. This component labels the answer with a scoreindicating the frequency of that sentiment. For example, if “meningitis”was almost always associated negative sentiment in the corpus, then thatanswer would be labeled with a numerical feature indicating thisrelationship.

An additional component of systems and methods herein (operating in item304) includes a structured knowledge resource containing relationshipsbetween entities and emotionally significant phenomena. This componentlabels answers linked to those phenomena with one or more numericalfeatures indicating the emotional content of the answer. For example, ifsome ontology indicates that meningitis causes severe headaches, andsevere headaches are known to be associated with negative emotions, theanswer “meningitis” would be labeled with a feature indicating arelationship to negative emotions.

A further component of systems and methods herein (operating in item304) includes a manually generated list of keywords that tend toco-occur in passages that suggest a negative emotional state keeps acount of how many times those keywords appear in the supporting passagesof that answer. For example, passages containing the words “death” and“pain” may be indicative of the emotional state associated with theanswer that this passage appears to support. Another component ofsystems and methods herein (operating in item 304) detects negativesentiment regarding a candidate answer in a supporting passage andlabels that answer with a numerical feature indicating the strength ofthe negative sentiment.

Also, one component of systems and methods herein (operating in item304) includes a precompiled model of the emotional states suggested by apassage. This component applies that model to the supporting passage andlabels the answer with the conclusion from that model. Such a model isprecompiled by manually labeling a set of sample passages as suggestingvarious emotional states and applying a text classification algorithm tobuild a model predictive of that manually labeled data. For example, amanually identified set of passages can involve angry patients, and thiscomponent uses a classification process to build a model that detectspassages involving angry patients. The component then applies that modelat question answering time to produce a feature indicative of whetherthe answer under consideration is suggestive of an angry patient.

Another component of systems and methods herein (operating in item 304)triggers use of one of a multitude of models of the emotional statessuggested by a passage (based on a one or more patient-specificfeatures). For example, a patient that is on a particular drug mayrequire a different model than a patient that is not on that drug,especially if the drug is known to have side effects on cognitiveprocessing, such as causing irritability. In such a case the featureindicative of whether the answer is suggestive of an angry patient willdepend on the drug as well as the content of the passage.

In item 306, if f(E) (emotional impact of the answer) is greater than apredetermined threshold value (Thresh) then processing proceeds to item310 to enter a more “empathetic mode.” Otherwise, processing proceeds toitem 308, where the question/answer system outputs the answer from item302 in an unaltered manner.

In item 306, even though a single threshold is given in order to enterthe emphatic mode, in other embodiments a continuum value of f(E) can beused to shape the emphatic level of responses of the question/answersystem. Here, f is a function. For example, four alternatives forcomputation of f(E) can be used, and all four can depend on“characteristics that are predictive of a high emotional state.” Some ofthose characteristics may be available in the system as structuredinformation from a profile and others may be observed by the systemduring use. A third source of these characteristics is analysis embeddedin the question answering system itself.

As noted above, in item 310, the systems and methods herein conveyhealth information to the user using a relatively more empathetic mode(e.g., by altering the answer to use different language or to includedifferent or less information). In item 310, if a user has used naturallanguage with certain words that express anxiety, f(E) may be high, andthe empathetic mode is entered in which a more “caring” wording is usedfor conveying information.

As additional examples of how information is presented in item 310, somestudies show that younger patients prefer to be addressed by their firstname. Thus, the empathetic mode can use this information whendetermining the way in which to interact with a user of the system orwhen providing information. To the contrary, some studies show peopleolder than age 65 may not prefer this, and the empathetic mode can makeuse of these studies as well.

In item 310, the user interface can be a virtual universe (VU) that usesavatars (human-like or cartoon characters) and the characteristics ofthe VU and avatars can be made more empathetic depending on f(E). Withthe avatars of systems and methods herein, the vocal tones and bodylanguage can reflect a caring “bedside manner.” Words, tone, volume,avatar appearance, etc., are all parameters that can be affected byempathy. For example, in a virtual universe (VU) of systems and methodsherein, avatars can automatically change appearance and vocalcharacteristics based on f(E). Avatars of systems and methods herein canaddress users in ways in which information can be assimilated andexchanged. For example, the avatar can chance appearance, based on f(E),with respect to clothing, facial characteristics, height, timbre ofvoice, gender, etc. Avatars of systems and methods herein can alsochange motion characteristics (e.g., gestures, pacing, head movements,hand movements, etc., based on f(E). In addition, f(E) can be controlthe scenery in the VU (e.g., hospital, island, landscape).

Also, item 310 includes a virtual universe switching element. With thevirtual switching element the system can alter the modality (e.g.,email, instant messaging, avatar in a virtual universe, etc.) in whichinformation if conveyed (based on f(E)). For example, certaininformation may be conveyed better by an avatar that may gesture to itsbody, and the system can automatically switch the user to a virtualuniverse when conveying such information.

In item 312, the systems and methods herein can optionally suggest apossible physician that the user may want to contact for moreinformation (using empathetic mode). In item 312, the systems andmethods herein can, for example, suggest healthcare professionals whohave been rated as having higher empathy ratings than other healthcareprofessional.

In item 314, information of the patient's emotional state can be outputto healthcare professionals. Thus, the systems and methods hereinformulate strategies for healthcare professionals to present unfavorable(or unwelcome) information in such a way that minimizes stress, whilebenefiting the patient. In addition, in item 314, a suggested tone maybe provided in a physician's report, alerting the healthcare providerthat the patient is in a specific emotional state (and therefore moreempathetic tone should be employed). Also, the assessed emotional stateof the patient can be provided as an additional determinant of thediagnosis itself in item 314, suggesting different treatments to thephysician depending on these assessed emotional states. Further, in item314, the systems and methods herein can output information to staff, whomay have access to patient rosters, annotated with question/answersuggested tones and levels of empathy. Item 314 can also provide adisplay (sign) to be used on the door or bedside of the patient, and thesign can provide coded information to all providers who enter the roomabout the suggested tone and level of empathy. This information isespecially useful to nurses, who often interact with patients more thandoctors, and therefore can have a greater impact on how a patient'spsychological state evolves or deteriorates.

Further, in item 316, an optional user rating system and analysis can beincorporated for auto-tuning the process.

Therefore, systems and methods herein include an emotion-assessmentcomponent and an information-output component with information changingbased on input from the emotion-assessment component (e.g., iff(E)>Thresh, then enter a more “empathetic mode”). With systems andmethods herein, the information can be a suggestion for a healthcareprofessional, and the information can be an input to the question/answersystem and aid in diagnosis and/or prescription of a treatment. Also,the information can be used to change avatar characteristics (e.g.,avatar appearance, motions, and vocal characteristics in a virtualuniverse) and/or scenery (e.g., hospital vs. forest). Additionally, theinformation can be used to automatically switch the user to a virtualuniverse. For example, certain information may be conveyed better by anavatar that may gesture to its body.

The function (f(E)) used to control the mode can take the form of aspecialized, ad hoc computation, or take the form of a formal logicalspecification that is interpreted by a general-purpose automated ruleengine, and such features can be combined with concrete examples ofdesired behavior via statistical machine learning.

While the foregoing discusses the systems and methods herein using apatient/healthcare provider example, FIG. 2 is a flowchart illustratingthe method and systems herein in a more generic manner applicable to allcomputerized question/answer systems. In item 350, these methods receivean inquiry or query from a user into a computerized device. Such methodsautomatically perform an initial analysis of the inquiry using thecomputerized device to produce potential answers to the inquiry fromevidence sources in item 352. Also, such methods can automaticallyobtain biometric information from biometric devices operativelyconnected to the computerized device in item 354.

As shown in item 356, these methods automatically analyze words used inthe inquiry, the potential answers, data maintained by the evidencesources, the biometric information, etc., using the computerized deviceto determine the sensitivity level associated with the inquiry, in item358. For example, in item 356 these methods can automatically identifywords associated with a specific emotional or cognitive state. Suchmethods can also automatically identify sentence structure associatedwith a specific emotional or cognitive state, or automatically identifyrelationships between data maintained by the evidence sources andemotionally significant phenomena. Such methods can also automaticallydetect negative sentiment associated with one of the potential answers.The sensitivity level associated with the inquiry 358 thus represents anemotional and cognitive state of the user as automatically determined bythe computerized device.

In item 360, such methods can automatically generate at least onefollow-up question based on the sensitivity level associated with theinquiry using the computerized device to refine the potential answers.When generating the follow-up question in item 360, these methods cangenerate relatively more empathetic follow-up questions or relativelyless empathetic follow-up questions based on the sensitivity levelassociated with the inquiry. The relatively more empathetic follow-upquestions and the relatively less empathetic follow-up questions aredistinguished from each other by the amount of information presented andby the word choice used when presenting the information.

Then, these methods automatically output the follow-up question(s) usingthe computerized device in response to the inquiry and, in turn, receiveat least one follow-up response into the computerized device in responseto the follow-up question(s) in item 362. In one example, these methodscan output the follow-up questions using an avatar. The avatar changesappearance and mannerisms based on the sensitivity level associated withthe inquiry.

As shown in item 364, such methods automatically analyze words used inthe follow-up response using the computerized device to refine thesensitivity level associated with the inquiry. Such methods alsoautomatically produce scores for the potential answers using thecomputerized device based on the inquiry, the follow-up responses, theratings of the evidence sources, etc. In item 366, these methodsdetermine whether the sensitivity level (again represented by thefunction f(E)) exceeds the predetermined threshold (Thresh).

Following this, these methods automatically generate output answers tothe inquiry based on the sensitivity level associated with the inquiryusing the computerized device to refine the potential answers. Whengenerating the output answers, based on the sensitivity level associatedwith the inquiry, such methods generate and output relatively moreempathetic output answers in item 370 if the function f(E)) exceeds thepredetermined threshold (Thresh); or generate and output relatively lessempathetic output answers in item 368 if the function f(E)) does notexceed the predetermined threshold (Thresh). Again, the relatively moreempathetic output answers and the relatively less empathetic outputanswers are distinguished from each other by the amount of informationpresented and the word choice used when presenting information.

With the answers in items 368 and 370, such methods automatically outputthe scores and links to the evidence sources from the computerizeddevice. Again, in items 368 and 370, these methods can output theanswers, the scores, the links, etc., using an avatar. In addition toproviding different forms of answers, the avatar changes appearance,background, mannerisms, etc., based on the sensitivity level associatedwith the inquiry. In addition, such methods can output the emotional andcognitive state of the user as automatically determined by thecomputerized device to individuals other than the user, and can makerecommendations to such individuals based on the emotional and cognitivestate of the user, in item 372.

Question-answer systems are illustrated in the following drawings. Suchsystems comprise a graphic user interface receiving an inquiry from auser, biometric devices receiving biometric information from the user,and a processor operatively connected to the graphic user interface andbiometric devices. The processor automatically performs an initialanalysis of the inquiry to produce potential answers to the inquiry fromevidence sources.

The processor automatically analyzes words used in the inquiry, thepotential answers, data maintained by the evidence sources, thebiometric information, etc., to determine the sensitivity levelassociated with the inquiry. For example, the processor canautomatically identify words associated with a specific emotional orcognitive state. Such a processor can also automatically identifysentence structure associated with a specific emotional or cognitivestate, or automatically identify relationships between data maintainedby the evidence sources and emotionally significant phenomena. Theprocessor can also automatically detect negative sentiment associatedwith one of the potential answers. The sensitivity level associated withthe inquiry thus represents an emotional and cognitive state of the useras automatically determined by the computerized device.

The processor can further generate at least one follow-up question basedon the sensitivity level associated with the inquiry to refine thepotential answers. When generating the follow-up question(s), theprocessor can generate relatively more empathetic follow-up questions orrelatively less empathetic follow-up questions based on the sensitivitylevel associated with the inquiry. The relatively more empatheticfollow-up questions and the relatively less empathetic follow-upquestions are distinguished from each other by the amount of informationpresented and by the word choice used when presenting the information.

Then, the graphic user interface automatically outputs the follow-upquestion(s) in response to the inquiry and, in turn, receives at leastone follow-up response in response to the follow-up question(s). In oneexample, the graphic user interface can output the follow-up questionsusing an avatar. The avatar changes appearance and mannerisms based onthe sensitivity level associated with the inquiry.

The processor automatically analyzes words used in the follow-upresponse to refine the sensitivity level associated with the inquiry.The processor also automatically produces scores for the potentialanswers based on the inquiry, the follow-up responses, the ratings ofthe evidence sources, etc.

Following this, the processor automatically generates output answers tothe inquiry based on the sensitivity level associated with the inquiryto refine the potential answers. When generating the output answers, theprocessor generates relatively more empathetic output answers orrelatively less empathetic output answers based on the sensitivity levelassociated with the inquiry. Again, the relatively more empatheticoutput answers and the relatively less empathetic output answers aredistinguished from each other by the amount of information presented andthe word choice used when presenting information.

Then, the graphic user interface automatically outputs the outputanswers, the scores, and links to the evidence sources from thecomputerized device. Again, the graphic user interface can output theanswers, the scores, the links, etc., using an avatar. The avatarchanges appearance and mannerisms based on the sensitivity levelassociated with the inquiry. In addition, the graphic user interface canoutput the emotional and cognitive state of the user as automaticallydetermined by the computerized device to individuals other than theuser, and can make recommendations to such individuals based on theemotional and cognitive state of the user.

As will be appreciated by one skilled in the art, aspects of the systemsand methods herein may be embodied as a system, method or computerprogram product. Accordingly, aspects of the present disclosure may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, etc.) oran embodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable non-transitorymedium(s) may be utilized. The computer readable medium may be acomputer readable signal medium or a computer readable storage medium.The non-transitory computer storage medium stores instructions, and aprocessor executes the instructions to perform the methods describedherein. A computer readable storage medium may be, for example, but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. More specific examples (a non-exhaustivelist) of the computer readable storage medium would include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments herein.It will be understood that each block of the flowchart illustrationsand/or two-dimensional block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

A representative hardware environment for practicing the embodimentsherein is depicted in FIG. 3. This schematic drawing illustrates ahardware configuration of an information handling/computer system inaccordance with the embodiments herein. The system comprises at leastone processor or central processing unit (CPU) 10. The CPUs 10 areinterconnected via system bus 12 to various devices such as a randomaccess memory (RAM) 14, read-only memory (ROM) 16, and an input/output(I/O) adapter 18. The I/O adapter 18 can connect to peripheral devices,such as disk units 11 and tape drives 13, or other program storagedevices that are readable by the system. The system can read theinventive instructions on the program storage devices and follow theseinstructions to execute the methodology of the embodiments herein. Thesystem further includes a user interface adapter 19 that connects akeyboard 15, mouse 17, speaker 24, microphone 22, and/or other userinterface devices such as a touch screen device (not shown) to the bus12 to gather user input. Additionally, a communication adapter 20connects the bus 12 to a data processing network 25, and a displayadapter 21 connects the bus 12 to a display device 23 which may beembodied as an output device such as a monitor, printer, or transmitter,for example.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments herein. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

Deployment types include loading directly in the client, server andproxy computers via loading a storage medium such as a CD, DVD, etc. Theprocess software may also be automatically or semi-automaticallydeployed into a computer system by sending the process software to acentral server or a group of central servers. The process software isthen downloaded into the client computers that will execute the processsoftware. The process software is sent directly to the client system viae-mail. The process software is then either detached to a directory orloaded into a directory by a button on the e-mail that executes aprogram that detaches the process software into a directory. Send theprocess software directly to a directory on the client computer harddrive. When there are proxy servers, the process will, select the proxyserver code, determine on which computers to place the proxy servers'code, transmit the proxy server code, and then install the proxy servercode on the proxy computer. The process software will be transmitted tothe proxy server then stored on the proxy server.

While it is understood that the process software may be deployed bymanually loading directly in the client, server and proxy computers vialoading a storage medium such as a CD, DVD, etc., the process softwaremay also be automatically or semi-automatically deployed into a computersystem by sending the process software to a central server or a group ofcentral servers. The process software is then downloaded into the clientcomputers that will execute the process software. Alternatively, theprocess software is sent directly to the client system via e-mail. Theprocess software is then either detached to a directory or loaded into adirectory by a button on the e-mail that executes a program thatdetaches the process software into a directory. Another alternative isto send the process software directly to a directory on the clientcomputer hard drive. When there are proxy servers, the process will,select the proxy server code, determine on which computers to place theproxy servers' code, transmit the proxy server code, then install theproxy server code on the proxy computer. The process software will betransmitted to the proxy server then stored on the proxy server.

In FIG. 4, Step 100 begins the deployment of the process software. Thefirst thing is to determine if there are any programs that will resideon a server or servers when the process software is executed 101. Ifthis is the case then the servers that will contain the executables areidentified 209. The process software for the server or servers istransferred directly to the servers' storage via FTP or some otherprotocol or by copying through the use of a shared file system 210. Theprocess software is then installed on the servers 211.

Next, a determination is made on whether the process software is bedeployed by having users access the process software on a server orservers 102. If the users are to access the process software on serversthen the server addresses that will store the process software areidentified 103.

A determination is made if a proxy server is to be built 200 to storethe process software. A proxy server is a server that sits between aclient application, such as a Web browser, and a real server. Itintercepts all requests to the real server to see if it can fulfill therequests itself. If not, it forwards the request to the real server. Thetwo primary benefits of a proxy server are to improve performance and tofilter requests. If a proxy server is required, then the proxy server isinstalled 201. The process software is sent to the servers either via aprotocol such as FTP or it is copied directly from the source files tothe server files via file sharing 202. Another embodiment would be tosend a transaction to the servers that contained the process softwareand have the server process the transaction, then receive and copy theprocess software to the server's file system. Once the process softwareis stored at the servers, the users via their client computers, thenaccess the process software on the servers and copy to their clientcomputers file systems 203. Another embodiment is to have the serversautomatically copy the process software to each client and then run theinstallation program for the process software at each client computer.The user executes the program that installs the process software on hisclient computer 212, then exits the process 108.

In step 104 a determination is made whether the process software is tobe deployed by sending the process software to users via e-mail. The setof users where the process software will be deployed are identifiedtogether with the addresses of the user client computers 105. Theprocess software is sent via e-mail to each of the users' clientcomputers 204. The users then receive the e-mail 205 and then detach theprocess software from the e-mail to a directory on their clientcomputers 206. The user executes the program that installs the processsoftware on his client computer 212, then exits the process 108.

Lastly, a determination is made on whether to the process software willbe sent directly to user directories on their client computers 106. Ifso, the user directories are identified 107. The process software istransferred directly to the user's client computer directory 207. Thiscan be done in several ways such as but not limited to sharing of thefile system directories and then copying from the sender's file systemto the recipient user's file system or alternatively using a transferprotocol such as File Transfer Protocol (FTP). The users access thedirectories on their client file systems in preparation for installingthe process software 208. The user executes the program that installsthe process software on his client computer 212 then exits the process108.

The process software is integrated into a client, server and networkenvironment by providing for the process software to coexist withapplications, operating systems and network operating systems softwareand then installing the process software on the clients and servers inthe environment where the process software will function.

The first step is to identify any software on the clients and serversincluding the network operating system where the process software willbe deployed that are required by the process software or that work inconjunction with the process software. This includes the networkoperating system that is software that enhances a basic operating systemby adding networking features.

Next, the software applications and version numbers will be identifiedand compared to the list of software applications and version numbersthat have been tested to work with the process software. Those softwareapplications that are missing or that do not match the correct versionwill be upgraded with the correct version numbers. Program instructionsthat pass parameters from the process software to the softwareapplications will be checked to ensure the parameter lists matches theparameter lists required by the process software. Conversely, parameterspassed by the software applications to the process software will bechecked to ensure the parameters match the parameters required by theprocess software. The client and server operating systems including thenetwork operating systems will be identified and compared to the list ofoperating systems, version numbers and network software that have beentested to work with the process software. Those operating systems,version numbers and network software that do not match the list oftested operating systems and version numbers will be upgraded on theclients and servers to the required level.

After ensuring that the software, where the process software is to bedeployed, is at the correct version level that has been tested to workwith the process software, the integration is completed by installingthe process software on the clients and servers.

In FIG. 5, step 220 begins the integration of the process software. Thefirst thing is to determine if there are any process software programsthat will execute on a server or servers 221. If this is not the case,then integration proceeds to 227. If this is the case, then the serveraddresses are identified 222. The servers are checked to see if theycontain software that includes the operating system (OS), applications,and network operating systems (NOS), together with their versionnumbers, which have been tested with the process software 223. Theservers are also checked to determine if there is any missing softwarethat is required by the process software 223.

A determination is made if the version numbers match the version numbersof OS, applications and NOS that have been tested with the processsoftware 224. If all of the versions match and there is no missingrequired software the integration continues in 227.

If one or more of the version numbers do not match, then the unmatchedversions are updated on the server or servers with the correct versions225. Additionally, if there is missing required software, then it isupdated on the server or servers 225. The server integration iscompleted by installing the process software 226.

Step 227 which follows either 221, 224, or 226, determines if there areany programs of the process software that will execute on the clients.If no process software programs execute on the clients, the integrationproceeds to 230 and exits. If this not the case, then the clientaddresses are identified 228.

The clients are checked to see if they contain software that includesthe operating system (OS), applications, and network operating systems(NOS), together with their version numbers, which have been tested withthe process software 229. The clients are also checked to determine ifthere is any missing software that is required by the process software229.

A determination is made as to whether the version numbers match theversion numbers of OS, applications and NOS that have been tested withthe process software 231. If all of the versions match and there is nomissing required software, then the integration proceeds to 230 andexits.

If one or more of the version numbers do not match, then the unmatchedversions are updated on the clients with the correct versions 232. Inaddition, if there is missing required software then it is updated onthe clients 232. The client integration is completed by installing theprocess software on the clients 233. The integration proceeds to 230 andexits.

The process software can be stored on a shared file system accessiblefrom one or more servers. The process software is executed viatransactions that contain data and server processing requests that useCPU units on the accessed server. CPU units are units of time such asminutes, seconds, hours on the central processor of the server.Additionally, the assessed server may make requests of other serversthat require CPU units. CPU units are an example that represents but onemeasurement of use. Other measurements of use include, but are notlimited to, network bandwidth, memory usage, storage usage, packettransfers, complete transactions etc. When multiple customers use thesame process software application, their transactions are differentiatedby the parameters included in the transactions that identify the uniquecustomer and the type of service for that customer. All of the CPU unitsand other measurements of use that are used for the services for eachcustomer are recorded. When the number of transactions to any one serverreaches a number that begins to affect the performance of that server,other servers are accessed to increase the capacity and to share theworkload. Likewise, when other measurements of use such as networkbandwidth, memory usage, storage usage, etc., approach a capacity so asto affect performance, additional network bandwidth, memory usage,storage etc., are added to share the workload. The measurements of useused for each service and customer are sent to a collecting server thatsums the measurements of use for each customer for each service that wasprocessed anywhere in the network of servers that provide the sharedexecution of the process software. The summed measurements of use unitsare periodically multiplied by unit costs and the resulting totalprocess software application service costs are alternatively sent to thecustomer and or indicated on a web site accessed by the customer, whichthen remits payment to the service provider. In another embodiment, theservice provider requests payment directly from a customer account at abanking or financial institution. In another embodiment, if the serviceprovider is also a customer of the customer that uses the processsoftware application, the payment owed to the service provider isreconciled to the payment owed by the service provider to minimize thetransfer of payments.

The process software is shared, simultaneously serving multiplecustomers in a flexible, automated fashion. It is standardized,requiring little customization and it is scalable, providing capacity ondemand in a pay-as-you-go model.

In FIG. 6, step 240 begins the On Demand process. A transaction iscreated than contains the unique customer identification, the requestedservice type and any service parameters that further specify the type ofservice 241. The transaction is then sent to the main server 242. In anOn Demand environment the main server can initially be the only server,then as capacity is consumed, other servers are added to the On Demandenvironment.

The server central processing unit (CPU) capacities in the On Demandenvironment are queried 243. The CPU requirement of the transaction isestimated, then the servers available CPU capacity in the On Demandenvironment are compared to the transaction CPU requirement to see ifthere is sufficient CPU available capacity in any server to process thetransaction 244. If there is not sufficient server CPU availablecapacity, then additional server CPU capacity is allocated to processthe transaction 248. If there was already sufficient Available CPUcapacity, then the transaction is sent to a selected server 245.

Before executing the transaction, a check is made of the remaining OnDemand environment to determine if the environment has sufficientavailable capacity for processing the transaction. This environmentcapacity consists of such things as but not limited to networkbandwidth, processor memory, storage, etc., 246. If there is notsufficient available capacity, then capacity will be added to the OnDemand environment 247. Next, the required software to process thetransaction is accessed, loaded into memory, then the transaction isexecuted 249.

The usage measurements are recorded 250. The usage measurements consistof the portions of those functions in the On Demand environment that areused to process the transaction. The usage of such functions is, but notlimited to, network bandwidth, processor memory, storage and CPU cyclesare what is recorded. The usage measurements are summed, multiplied byunit costs, and then recorded as a charge to the requesting customer251. If the customer has requested that the On Demand costs be posted toa web site 252, then they are posted 253.

If the customer has requested that the On Demand costs be sent viae-mail to a customer address 254, then they are sent 255. If thecustomer has requested that the On Demand costs be paid directly from acustomer account 256, then payment is received directly from thecustomer account 257. The last step is to exit the On Demand process258.

The process software may be deployed, accessed and executed through theuse of a virtual private network (VPN), which is any combination oftechnologies that can be used to secure a connection through anotherwise unsecured or untrusted network. The use of VPNs is to improvesecurity and for reduced operational costs. The VPN makes use of apublic network, usually the Internet, to connect remote sites or userstogether. Instead of using a dedicated, real-world connection such asleased line, the VPN uses “virtual” connections routed through theInternet from the company's private network to the remote site oremployee.

The process software may be deployed, accessed and executed througheither a remote-access or a site-to-site VPN. When using theremote-access VPNs the process software is deployed, accessed andexecuted via the secure, encrypted connections between a company'sprivate network and remote users through a third-party service provider.The enterprise service provider (ESP) sets a network access server (NAS)and provides the remote users with desktop client software for theircomputers. The telecommuters can then dial a toll-free number or attachdirectly via a cable or DSL modem to reach the NAS and use their VPNclient software to access the corporate network and to access, downloadand execute the process software.

When using the site-to-site VPN, the process software is deployed,accessed and executed through the use of dedicated equipment andlarge-scale encryption that are used to connect a companies multiplefixed sites over a public network such as the Internet.

The process software is transported over the VPN via tunneling which isthe process of placing an entire packet within another packet andsending it over a network. The protocol of the outer packet isunderstood by the network and both points, called tunnel interfaces,where the packet enters and exits the network.

In FIGS. 7 and 8, step 260 begins the Virtual Private Network (VPN)process. A determination is made to see if a VPN for remote access isrequired 261. If it is not required, then proceed to 262. If it isrequired, then determine if the remote access VPN exists 264.

If it does exist, then proceed to 265. Otherwise, identify the thirdparty provider that will provide the secure, encrypted connectionsbetween the company's private network and the company's remote users276. The company's remote users are identified 277. The third partyprovider then sets up a network access server (NAS) 278 that allows theremote users to dial a toll free number or attach directly via a cableor DSL modem to access, download and install the desktop client softwarefor the remote-access VPN 279.

After the remote access VPN has been built, or if it been previouslyinstalled, the remote users can then access the process software bydialing into the NAS or attaching directly via a cable or DSL modem intothe NAS 265. This allows entry into the corporate network where theprocess software is accessed 266. The process software is transported tothe remote user's desktop over the network via tunneling 273. Theprocess software is divided into packets and each packet including thedata and protocol is placed within another packet 267. When the processsoftware arrives at the remote user's desktop, it is removed from thepackets, reconstituted and then is executed on the remote users desktop268.

A determination is made to see if a VPN for site to site access isrequired 262. If it is not required, then proceed to exit the process263. Otherwise, determine if the site to site VPN exists 269. If it doesexist, then proceed to 272. Otherwise, install the dedicated equipmentrequired to establish a site to site VPN 270. Then build the large scaleencryption into the VPN 271.

After the site to site VPN has been built, or if it had been previouslyestablished, the users access the process software via the VPN 272. Theprocess software is transported to the site users over the network viatunneling. That is, the process software is divided into packets andeach packet including the data and protocol is placed within anotherpacket 274. When the process software arrives at the remote user'sdesktop, it is removed from the packets, reconstituted and is executedon the site users desktop 275. Proceed to exit the process 263.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of this disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescriptions of the various embodiments of the present systems andmethods herein have been presented for purposes of illustration, but arenot intended to be exhaustive or limited to the embodiments disclosed.Many modifications and variations will be apparent to those of ordinaryskill in the art without departing from the scope and spirit of thedescribed embodiments. The terminology used herein was chosen to bestexplain the principles of the embodiments, the practical application ortechnical improvement over technologies found in the marketplace, or toenable others of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method comprising: receiving an inquiry into acomputerized device; automatically performing an initial analysis ofsaid inquiry, using said computerized device, said initial analysisproduces potential answers to said inquiry from evidence sources;automatically analyzing words used in said inquiry, using saidcomputerized device, to determine an emotional state of a user by atleast one of: automatically identifying words in said inquiry that areassociated with a specific emotional state; automatically identifyingsentence structure in said inquiry that is associated with a specificemotional state; automatically generating at least one response to saidinquiry based on said potential answers to format said response to berelatively more empathetic or relatively less empathetic based on saidemotional state; and outputting said response, said outputting comprisesoutputting said response to said user using an avatar, said avatarchanging appearance and mannerisms based on said emotional state,wherein said mannerisms include changing gestures, pacing, headmovements, and hand movements of said avatar.
 2. The method according toclaim 1, further comprising automatically obtaining biometricinformation from biometric devices operatively connected to saidcomputerized device and determining said emotional state based on saidbiometric information.
 3. The method according to claim 1, said responseis relatively more empathetic or relatively less empathetic based on bythe amount of information presented and word choice when presenting saidinformation.
 4. The method according to claim 1, said inquiry beingreceived from said user.
 5. The method according to claim 1, saidoutputting outputs said response and said emotional state to individualsother than said user.
 6. The method according to claim 1, furthercomprising making recommendations to individuals other than said userbased on said emotional state of said user.
 7. A method comprising:receiving an inquiry into a computerized device; automaticallyperforming an initial analysis of said inquiry, using said computerizeddevice, said initial analysis produces potential answers to said inquiryfrom evidence sources; automatically analyzing words used in saidpotential answers and data maintained by said evidence sources, usingsaid computerized device, to determine an emotional impact of saidpotential answers by at least one of: automatically identifying words insaid potential answers that are associated with a specific emotionalstate; automatically identifying sentence structure in said potentialanswers that is associated with a specific emotional state; andautomatically identifying relationships between data maintained by saidevidence sources and phenomena effecting emotional state; automaticallygenerating at least one response to said inquiry based on said potentialanswers to format said response to be relatively more empathetic orrelatively less empathetic based on said emotional impact of saidpotential answers; and outputting said response, said outputtingcomprises outputting said response to said user using an avatar, saidavatar changing appearance and mannerisms based on said emotional state,wherein said mannerisms include changing gestures, pacing, headmovements, and hand movements of said avatar.
 8. The method according toclaim 7, further comprising determining said emotional impact of saidpotential answers based on at least one of: a manually generated list ofresponses that are known to be associated with various emotional statesin response to various types of questions; an automatically mined listof terms that occur disproportionately in said response indicatingnegative sentiment; a structured knowledge resource containingrelationships between entities and phenomena effecting emotional state;a manually generated list of keywords that tend to co-occur in saidresponse that suggest a negative emotional state and keeping a count ofhow many times said keywords appear in said response; a precompiledmodel of the emotional states suggested by said response; and aplurality of models of emotional states suggested by said response basedon one or more features associated with a specific patient.
 9. Themethod according to claim 7, said response is relatively more empatheticor relatively less empathetic based on by the amount of informationpresented and word choice when presenting said information.
 10. Themethod according to claim 7, said inquiry being received from said user.11. The method according to claim 7, said outputting outputs saidresponse and said emotional state to individuals other than said user.12. The method according to claim 7, further comprising makingrecommendations to individuals other than said user based on saidemotional state of said user.
 13. A method comprising: receiving aninquiry into a computerized device; automatically performing an initialanalysis of said inquiry, using said computerized device, said initialanalysis produces potential answers to said inquiry from evidencesources; automatically analyzing words used in said inquiry, saidpotential answers, and data maintained by said evidence sources, usingsaid computerized device, to determine an emotional state of a user andan emotional impact of said potential answers by at least one of:automatically identifying words in said inquiry and said potentialanswers that are associated with a specific emotional state;automatically identifying sentence structure in said inquiry and saidpotential answers that is associated with a specific emotional state;and automatically identifying relationships between data maintained bysaid evidence sources and phenomena effecting emotional state;automatically generating at least one response to said inquiry based onsaid potential answers to format said response to be relatively moreempathetic or relatively less empathetic based on said emotional stateand said emotional impact of said potential answers; and outputting saidresponse, said outputting comprises outputting said response to saiduser using an avatar, said avatar changing appearance and mannerismsbased on said emotional state, wherein said mannerisms include changinggestures, pacing, head movements, and hand movements of said avatar. 14.The method according to claim 13, further comprising determining saidemotional state and said emotional impact of said potential answersbased on at least one of: a manually generated list of responses thatare known to be associated with various emotional states in response tovarious types of questions; an automatically mined list of terms thatoccur disproportionately in said response indicating negative sentiment;a structured knowledge resource containing relationships betweenentities and phenomena effecting emotional state; a manually generatedlist of keywords that tend to co-occur in said response that suggest anegative emotional state and keeping a count of how many times saidkeywords appear in said response; a precompiled model of the emotionalstates suggested by said response; and a plurality of models ofemotional states suggested by said response based on one or morefeatures associated with a specific patient.
 15. The method according toclaim 13, said response is relatively more empathetic or relatively lessempathetic based on by the amount of information presented and wordchoice when presenting said information.
 16. The method according toclaim 13, said inquiry being received from said user.
 17. The methodaccording to claim 13, said outputting outputs said response and saidemotional state to individuals other than said user.
 18. The methodaccording to claim 13, further comprising making recommendations toindividuals other than said user based on said emotional state of saiduser.